Boosting Model Performance in Long Tail Distribution Learning with Synthetic Data (07/03)

To achieve good performance across various tasks, it’s crucial for models to effectively learn the representation of each class. In most cases of model training, there’s a bias towards learning the head-class, which leads to significant performance degradation in the tail-class.

The Impact of Long Tail on Machine Learning 

Long Tail Distribution refers to a pattern in data where a significant proportion of items have low frequencies compared to a small number of highly frequent items. This distribution consists of a “head” with high-frequency minority items and a “tail” with low-frequency majority items. The phenomenon of Long Tail Distribution is prevalent across various datasets, indicating that real-world datasets often encompass a diverse array of items. Long Tail Distribution poses several challenges for machine learning models.

While models can effectively learn high-frequency items, they often struggle with low-frequency items, leading to diminished performance. This tendency can result in models focusing solely on major patterns while neglecting rare patterns. Therefore, effective strategies are necessary to address Long Tail Distribution by supplementing data scarcity and enabling models to learn the entire distribution.

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Benefits of Using Synthetic Data for Learning Long Tail Distribution

Synthetic data is a crucial tool for addressing the Long Tail Distribution problem. Actual data often suffers from limitations and imbalance, especially with insufficient data for rare items. Synthetic data resolves these data scarcity issues and helps models learn diverse patterns effectively.

• Increased Data Diversity: Synthetic data helps models learn a variety of situations and patterns. This enhances the generalization performance of the model and increases its predictive accuracy for rare items.
• Mitigated Data Imbalance: Synthetic data adds more instances of infrequent items, thereby alleviating the problem of data imbalance. This enables the model to better learn and predict rare items.
• Improved Model Performance: Utilizing synthetic data improves the overall performance of the model. This is particularly beneficial for increasing accuracy in predicting rare items.

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